
Incident Post-Mortem
OfficialFreeCreate structured, blameless post-mortems for incidents.
Free · Opens the source repo
What Incident Post-Mortem does
The Incident Post-Mortem skill is designed to assist teams in documenting and analyzing production incidents in a structured manner. When an outage or significant service degradation occurs, this skill guides users through the process of writing a blameless post-mortem report. It emphasizes understanding the root causes of incidents without placing blame on individuals, fostering a culture of learning and improvement. The skill triggers on various phrases related to incident reviews, ensuring that teams can quickly access the guidance they need during critical times.
This skill offers a comprehensive framework for gathering essential incident metadata, including impact details, timeline events, and contributing factors. It encourages teams to reconstruct the incident timeline accurately, ensuring that all relevant events are captured. By utilizing the 5 Whys technique, teams can delve deep into the root causes of incidents, identifying systemic issues that need addressing. This structured approach not only aids in documenting what went wrong but also helps in quantifying the impact of the incident, providing a clearer picture of the consequences.
In addition to the analysis, the skill facilitates the generation of actionable items for improvement, assigning ownership and due dates to ensure accountability. This ensures that lessons learned from the incident are translated into concrete actions that can prevent future occurrences. The output is a well-structured post-mortem document that serves as a valuable resource for the team and the organization, promoting transparency and continuous improvement.
The Incident Post-Mortem skill is ideal for engineering teams, incident response teams, and any organization that values learning from failures. It is particularly useful in high-stakes environments where understanding incidents is critical for maintaining service reliability and user trust.
When to use it
Use this skill after resolving a production outage or significant service degradation to create a blameless post-mortem.
When not to use it
This skill is not suitable for minor bugs caught in staging or planned maintenance windows where no significant learning occurred.
What you can build with it
Production Outage Documentation
After a major service outage, use this skill to document the incident and analyze what went wrong, ensuring the team learns from the experience.
Service Degradation Analysis
When a service experiences significant degradation, this skill helps the team create a post-mortem to understand the impact and prevent future occurrences.
Near-Miss Incident Review
If a significant near-miss occurs, this skill guides the team in capturing learnings before the context fades, helping to strengthen incident response processes.
How to install Incident Post-Mortem
View source1. Install with the skills CLI
npx skills add github/awesome-copilot/incident-postmortem --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by githubIncident Post-Mortem
Guide a team through writing a structured, blameless post-mortem after a production incident. The output is a document that builds shared understanding, identifies root causes without blame, and produces concrete action items to prevent recurrence.
Blameless Principle
Systems fail, not people. The goal is to understand HOW the incident happened — not WHO caused it. Avoid language like "X forgot to", "Y should have known". Use "the system did not", "the process lacked", "the alert did not fire".
When to Use
- Production outage or service degradation has been resolved
- A significant near-miss occurred (would have been an incident if caught later)
- User-facing errors, data loss, or SLA breach happened
- Team wants to capture learnings before context fades
Not for: Minor bugs caught in staging, planned maintenance windows, or incidents with no learning value.
Input Requirements
Gather these details before writing the post-mortem. Ask for anything missing:
Incident Metadata
- Incident title (short, descriptive)
- Date and time of detection (with timezone)
- Date and time of resolution
- Severity / impact level (P1–P4 or equivalent)
- Incident commander / on-call owner
Impact
- Affected services and systems
- User-facing impact (errors, slowness, full outage)
- Estimated number of users affected
- Data loss or corruption (yes/no, scope)
- SLA/SLO breach (yes/no, by how much)
Timeline Events
Key moments to reconstruct:
- First symptom occurred
- Alert fired (or was noticed manually)
- On-call paged / incident declared
- Investigation started
- Root cause identified
- Mitigation applied
- Full resolution confirmed
- Customer communication sent (if any)
Contributing Factors
Ask the team: "What made this worse than it needed to be?" — not "who failed". Examples:
- Alert threshold too high / alert didn't fire
- Runbook was missing or outdated
- Deploy lacked a feature flag for rollback
- Monitoring didn't cover this failure mode
- On-call handoff missed context
Process
Step 1 — Gather Metadata
If the user has not provided full incident details, ask for them section by section. Don't proceed to writing until you have: title, times, severity, affected services, and at least a rough timeline.
Step 2 — Reconstruct Timeline
Work with the user to build a precise chronological timeline. For each event:
- Exact time (UTC preferred)
- What happened (system event or human action)
- Who observed it or took the action
- Link to log / alert / Slack message if available
Flag gaps: "We don't know what happened between 14:32 and 14:47 — worth checking logs."
Step 3 — Root Cause Analysis
Use the 5 Whys iteratively:
Why did users see 500 errors?
→ The API pods were crash-looping.
Why were they crash-looping?
→ Memory limit was exceeded.
Why was the limit exceeded?
→ A new query was loading full result sets into memory.
Why wasn't this caught before deploy?
→ Load tests only covered the p50 case, not high-cardinality accounts.
Why did load tests only cover p50?
→ We had no test fixtures for large accounts.
Stop when you reach a system/process gap you can fix. The last "why" should point to an action item.
Distinguish:
- Root cause — the deepest systemic gap (one or two)
- Contributing factors — conditions that made it worse but aren't the root cause
Step 4 — Impact Quantification
Help the user be precise:
- Duration: detection to resolution (not symptom start to resolution — separate these)
- Error rate at peak vs. normal baseline
- Percentage of traffic affected
- Revenue / business impact if known
Step 5 — Action Items
For each root cause and contributing factor, generate at least one action item:
| # | Action | Owner | Due Date | Priority |
|---|---|---|---|---|
| 1 | Add load test fixtures for accounts > 10k records | @eng-team | 2026-07-01 | High |
| 2 | Lower memory alert threshold from 90% to 75% | @platform | 2026-06-23 | High |
| 3 | Add runbook for memory OOM pods | @on-call-rotation | 2026-06-30 | Medium |
Action items must have an owner (a person, not a team) and a due date. Vague actions like "improve monitoring" are not acceptable — break them into specific deliverables.
Step 6 — Write the Document
Produce the full post-mortem using the template below. Save to docs/postmortems/YYYY-MM-DD-<slug>.md.
Output Template
# Post-Mortem: [Incident Title]
**Date:** YYYY-MM-DD
**Severity:** P[1-4]
**Duration:** X hours Y minutes (HH:MM UTC – HH:MM UTC)
**Incident Commander:** @name
**Status:** Resolved
---
## Summary
[2–3 sentences. What happened, what was the user impact, how was it resolved. Written for someone who wasn't involved.]
## Impact
| Dimension | Value |
|-----------|-------|
| Affected services | [list] |
| User-facing impact | [errors / degraded / full outage] |
| Users affected | [estimated number or %] |
| Peak error rate | [X% vs Y% baseline] |
| Data loss | [none / describe scope] |
| SLA breach | [yes/no — by how much] |
## Timeline
All times UTC.
| Time | Event |
|------|-------|
| HH:MM | [First symptom / alert fired] |
| HH:MM | [On-call paged] |
| HH:MM | [Incident declared] |
| HH:MM | [Root cause identified] |
| HH:MM | [Mitigation applied] |
| HH:MM | [Full resolution confirmed] |
| HH:MM | [Customer communication sent] |
## Root Cause
[1–2 paragraphs. The deepest systemic gap that, if fixed, would have prevented the incident. Written in blameless language. Reference the 5 Whys chain if helpful.]
## Contributing Factors
- [Factor 1 — condition that made the incident worse]
- [Factor 2]
- [Factor 3]
## What Went Well
- [Thing that worked — good alert, fast response, clear runbook]
- [Another positive]
## What Could Have Gone Better
- [Gap in process, tooling, or coverage — no blame language]
- [Another gap]
## Action Items
| # | Action | Owner | Due Date | Priority |
|---|--------|-------|----------|----------|
| 1 | [Specific deliverable] | @person | YYYY-MM-DD | High/Medium/Low |
| 2 | | | | |
## Lessons Learned
[Optional. 2–4 bullet points capturing non-obvious insights worth sharing with the broader team.]
Common Mistakes
| Mistake | Fix |
|---|---|
| "Bob forgot to check the config" | "The deploy checklist did not include config validation" |
| Root cause is "human error" | Keep asking Why — human error is always a symptom |
| Action items without owners | Every item needs a named individual, not a team |
| Timeline reconstructed from memory | Check logs, alerts, Slack, PagerDuty before writing |
| "Improve monitoring" as an action | Specify: which service, which metric, what threshold, by when |
| Post-mortem written weeks later | Write within 48–72 hours while context is fresh |
Frequently asked questions about Incident Post-Mortem
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